HDL-IDS: A Hybrid Deep Learning Architecture for Intrusion Detection in the Internet of Vehicles

Sensors

Safi Ullah¹  Muazzam A. Khan¹  Zil e Huma²  Sajjad Shaukat Jamal³  Muhammad Tahir Hassan⁴  Arshad⁵  William J. Buchanan⁶  Nikolaos Pitropakis⁶  Jawad Ahmad⁶ 

[1] Department of Computer Science, Quaid-i-Azam University, Islamabad 44000, Pakistan;Department of Electrical Engineering, Institute of Space Technology, Islamabad 44000, Pakistan;Department of Mathematics, College of Science, King Khalid University, Abha 61413, Saudi Arabia;Department of Mechanical Engineering, Bahauddin Zakariya University, Multan 66000, Pakistan;Institute for Energy and Environment, University of Strathclyde, Glasgow G1 1XQ, UK;School of Computing, Edinburgh Napier University, Edinburgh EH10 5DT, UK;

关键词: deep learning;    gated recurrent units;    Internet of Things;    Internet of Vehicles;    long short-term memory;    machine learning;   

DOI  :  10.3390/s22041340

来源: DOAJ

【 摘 要 】

Internet of Vehicles (IoV) is an application of the Internet of Things (IoT) network that connects smart vehicles to the internet, and vehicles with each other. With the emergence of IoV technology, customers have placed great attention on smart vehicles. However, the rapid growth of IoV has also caused many security and privacy challenges that can lead to fatal accidents. To reduce smart vehicle accidents and detect malicious attacks in vehicular networks, several researchers have presented machine learning (ML)-based models for intrusion detection in IoT networks. However, a proficient and real-time faster algorithm is needed to detect malicious attacks in IoV. This article proposes a hybrid deep learning (DL) model for cyber attack detection in IoV. The proposed model is based on long short-term memory (LSTM) and gated recurrent unit (GRU). The performance of the proposed model is analyzed by using two datasets—a combined DDoS dataset that contains CIC DoS, CI-CIDS 2017, and CSE-CIC-IDS 2018, and a car-hacking dataset. The experimental results demonstrate that the proposed algorithm achieves higher attack detection accuracy of 99.5% and 99.9% for DDoS and car hacks, respectively. The other performance scores, precision, recall, and F1-score, also verify the superior performance of the proposed framework.

【 授权许可】

Unknown